Advanced Analytics in Central Banks:
Basic Assumptions and Preliminary Results of a Research Project
Matthias Goeken and Leni Hirdes
University of Applied Sciences of Deutsche Bundesbank, Hachenburg, Germany
Keywords: Central Banking, Advanced Analytics, Systematic Review.
Abstract: This position paper presents methodological considerations and ideas as well as preliminary results of a re-
search project. We argue that integrating design science (DS) and review research is useful for developing
artifacts that serve, for example, to share knowledge within a domain. Our project aims to support knowledge
sharing in central banking by means of systematic reviews. Such a consolidated body of knowledge can also
be considered an artifact in the sense of DS research. We demonstrate how such a review can be conducted
and what results it yields. Since it is not clear whether such an application of systematic reviews is relevant
and feasible, problem definition and objectives of the project are discussed.
1 INTRODUCTION
Central banks are knowledge-intensive organizations.
To fulfill their mandate, a critical task is to observe
and monitor markets and the economic environment,
to consider economic policy measures, to make
forecasts and to draw conclusions for potential
actions. To do so, they traditionally rely on statistics
and econometrics in connection with economic model
building. These approaches and methods are well
established and have been successfully applied in the
domain of central banking for many years.
In fact, central banks tend not to contest with other
organizations or with each other. Therefore, unlike
many players in the financial industry, they are not
dependent on gaining competitive advantages e.g., by
means of (technical) innovations. Nevertheless, they
are interested in expanding their analytical competen-
cies in order to be able to best fulfil their mandate.
And, like other players in the financial industry, they
strive to do so by adopting innovative and advanced
technologies and approaches, e.g. big data, machine
learning, AI, and data science (Bholat, 2015).
1
Seven years ago, a survey of central banks by the
Irving Fisher Committee (IFC, 2017) found that there
was already strong interest in big data, machine
1
In this paper we use “advanced analytics” as an
umbrella term to refer to big data (BD), machine
learning (ML), data science (DS), and artificial
learning and related topics in the central banking
community. In addition to the general trends fre-
quently noted for the economy as a whole (data avai-
lability, availability of superior computational power,
and more mature methods and procedures) and the
related general desire to improve analytics capabili-
ties in traditional areas, there are specific drivers that
are pertinent to the growing interest of central banks.
E.g., the Great Financial Crisis (GFC) of 2007-09 laid
bare the necessity of more disaggregated data (Doerr
et al. 2021). In this respect, macroprudential policy
has been established as a new field of activity
alongside monetary policy and banking supervision.
This is where the improved use of granular data on
individual economies for financial stability analysis
is a key task (Buch, 2019). Figure 1 illustrates the
growing interest among central banks in recent years.
It displays that the number of central bank speeches
mentioning “advanced analytics” and similar related
topics increased considerably from 2015 to 2020.
However, actual use by central banks has so far
remained limited. The IFC Annual Report 2017
identifies three reasons for this: first, a number of
operational challenges, in particular the availability of
significant (technical and human) resources to deal
with big data. Second, making adequate arrangements
for the management of data and information for
intelligence (AI). As the major parts of the paper are
about these approaches in general, a clear distinction is
not necessary in most parts of our paper.
Goeken, M. and Hirdes, L.
Advanced Analytics in Central Banks: Basic Assumptions and Preliminary Results of a Research Project.
DOI: 10.5220/0011306800003280
In Proceedings of the 19th International Conference on Smart Business Technologies (ICSBT 2022), pages 137-143
ISBN: 978-989-758-587-6; ISSN: 2184-772X
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
137
central banks; and third, that the use of big data for
policy purposes is not without risks, such as creating
a false sense of security and accuracy among the
audience or the public (IFC, 2017).
Figure 1: Central banks’ interest in big data (Doerr et al.,
2021).
These and other problem areas open room for
improvement. One sensible idea is to improve
knowledge sharing and avoid reinventing the wheel
multiple times. Hence, our approach is to summarize
and evaluate certain works, where experiences from
projects, case studies and applications are already
available. In this way, exchange of experience could
be strengthened – beyond the usual formats of central
bank cooperation.
For the purpose of consolidating existing and
published results, so-called systematic reviews have
become increasingly accepted as effective instru-
ments in the information systems discipline in recent
years (Webster & Watson, 2002; Wainwright et al.,
2018). Reviews are seen as powerful instruments to
enable the synthesis of prior findings, reconciliation
of inconsistent findings, and resolution of
relationships. In this realm, reference is also made to
the practical relevance of reviews.
For many reviews, the motivation is already given
by the fact that primary research exists. Here we are
faced with a domain for which it is not entirely
obvious whether a review that consolidates the body
of knowledge is meaningful, relevant, and feasible.
For this reason, the combination of review research
and design science seems appropriate.
In this paper, we first outline the rational and the
motivation for (part 2) and the methodological
background of our research project (part 3). Part 4
outlines first and priliminary results. We finish with
conclusions and and give hints for future research.
2 RELATED WORK
AI, Data Science and related topics have already been
the subject of review articles. Collins et al. (2021) ex-
amine the extent to which primary studies identify
business value of IT through AI and specific contri-
butions made by AI, and they consider the scientific
and practical implications of using AI in organiza-
tions. Abdel-Karim et al. (2021) investigate in their
review, why the “transfer of theoretical knowledge
from machine learning to applications that solve (in-
dustry-)specific problems” is only partly successful.
They do so with the goal of advancing IS theory as
well as to help practitioners in applying ML methods.
Other reviews explore the practical understanding of
AI to better identify areas of application and improve
adoption (Bawack et al. 2019).
Likewise, papers appear that investigate the “state
of the art” with a focus on methodological issues of
advanced analytics (Angra & Ahuja, 2017). Some
high-level reviews study the application of ML and
AI in general by (Warin & Stojkov, 2021).
Occasionally, but more rarely, there seem to be works
that examine the application of advanced analytics in
relation to a specific technical topic or domain. Our –
so far non-systematic – literature review e.g.
identified reviews on the application of ML and AI in
supply chain management or in the context of
manufacturing (Fahle et al. 2020; Younis et al.,
2021). However, such works are rather rare. It seems
that the focus on a domain or specific cases is much
more common in other disciplines, e.g. in medicine,
probably, because the benefits are more obvious.
We go with Abdel-Karim et al. (2021), who note
a low consideration of ML in the IS discipline.
However, like them, we assume that after remarkable
progress has been made in Computer Science from a
technical and methodological point of view, it is a
fundamental task of IS research to investigate the
transfer into companies and organizations and to
support it with appropriate methods and artifacts.
Hence, we focus on the transfer of theoretical
knowledge regarding advanced analytics to
applications. Accordingly, the focus is on sharing
knowledge for a practical purpose and action, which
is not the primary goal of existing reviews.
Since we can identify a concrete and relevant use
case in the central banking domain (see above), our
research project aims at the idea of conducting a
review of the application of advanced analytics for
this rather narrowly defined field. Last year,
Kinywamaghana & Steffen (2021) highlighted
examples of the application of the aforementioned
technologies and methods. To our knowledge,
however, there is currently no review that examines
the literature for the field of central banking
systematically and with a practical stance.
ICSBT 2022 - 19th International Conference on Smart Business Technologies
138
Phase Outcome
0. Building the Infrastructure Framework to represent the subject matter (taxonomy, ontology); classification system
1. Defining the research question Research Question
2. Searching the literature Preliminary inclusion of studies based on database research
3. Selecting the studies for inclusion Set of final eligible studies
4. Assessing the quality of included studies
and structuring of their results
Assessed and structured studies
5. Combining the results Representation of the gained and integrated results, e.g., in a 'Summary of findings'
table
6. Create a structured report Report on the findings and the evidence gained
Figure 2: Process for a Systematic Review (based on Cooper 2016; Goeken 2011).
Figure 3: DSR methodology process model (Peffers et al. (2007).
3 METHODOLOGICAL
BACKGROUND
Our research endeavour can be sensibly classified in
two ways. First, it is review research, since it ad-
dresses the stated goals (above all: synthesizing the
existing knowledge; representation of the “state of the
art” at a given point in time). Accordingly, it is based
on the now established and widely accepted process
for reviews, which is illustrated in figure 2 – phase 1
to 6. On the other hand, it is also design-oriented in
parts, since the result is intended to be an artifact that
provides support for practitioners (presenting the
“body of knowledge” for practical application and
practical usefulness). In this respect, it should also be
guided by approaches from Design Science. In this
field, the DSRM by Peffers et al. (2007) is considered
the “mostly widely referenced model” guiding the
process (Vom Brocke et al., 2020) – see figure 3.
Thus, we envisage to combine both approaches to
have the two sides of the coin in view – consolidation
of knowledge and representation of knowledge for
practical application. Due to page restrictions and
because we are presenting research in progress here,
emphasis is put in the following not on those aspects
that are already well discussed (e.g. the search
strategy) but on methodological considerations and
preliminary results of ours that are already somehow
advanced and that could lead to a fruitful discussion.
In short and certainly somewhat simplified terms,
we state that, while reviews are primarily required to
be transparent and reproducible in order to yield
purposeful and reliable results, the development of
artifacts focuses on making justified design decisions,
but also on ensuring that the final result is useful and
easy to use. Accordingly, the procedure for reviews
tends to be rather linear, while DSRM provides for
explicit feedback loops that not only allow but also
stipulate the return to previous phases, so that
continuous improvement is possible in an iterative
process. If a result is available in phase 3 (DSRM),
the process does not end, but the artifact is evaluated
after its demonstration. I.e. it is also adapted, changed
etc. after (initial) completion if necessary. Although
Advanced Analytics in Central Banks: Basic Assumptions and Preliminary Results of a Research Project
139
this is not explicitly discussed in the literature, a step-
by-step extension of the artifact might be introduced
to improve usefulness and maintain applicability of a
given artifact.
Although the definition of a research question is
relatively close to the first two phases of the DSRM
(problem motivation and definition of objectives of a
solution), a phase is added upstream here (phase 0):
Due to the fact that – compared with other disciplines,
e.g. medicine – IS lacks established nomenclatures
and accepted taxonomies of the subject matters, this
phase initially deals with the construction of a
framework (or multiple frameworks) for the research
topic at hand. The goal of this framework is to better
conceptualize the research question and potentially
decompose it in such detail as to allow for an
evolutionary approach. Due to the different nature of
this task and its importance, this is treated as a
separate phase. This construction is a design-oriented
task which itself could be based on design science
methodology accordingly (Peffers et al. 2007).
Intention of this phase is also to conceptually
atomize the research question so that the searching for
relevant literature as well as its structuring and
presentation can be supported. It also becomes
meaningful with a view to possible iterations. To
foresee multiple iterations makes sense against the
background of practicability (even in the quite
delimited domain of central banking, there are many
papers that may not be manageable due to their sheer
volume) and because in this way subject matter
experts for the different topics can be involved
(money laundering is just completely different from
financial stability).
4 SELECTED PRELIMINARY
RESULTS
In our approach, phases 0 and 1 were carried out iter-
atively. Problem identification and definition of ob-
jectives (DSRM) flow into this and lead to a high-
level research question, which is shaped with the cre-
ation of a framework (conceptual model or taxon-
omy). One focus of the research project is the moti-
vation (feasibility as well as the potentials of a collec-
tion of case studies and use cases on the application
of advanced analytics in central banks). Since central
banks traditionally have access to a significant
amount of data and statistics that are regularly col-
lected while fulfilling their mandate and inform their
decision-making processes, the project will explore
whether and to what extent such a collection would
be useful for central banks and whether it could help
to improve the decision making.
It might help central banks in their knowledge
sharing to gain a better understanding of what others
are researching or what is being done in general in the
topic area of advanced analytics. In classifying re-
search findings, a unified knowledge base could help
identify new datasets, associated methodologies, re-
lating projects, and assist central banks with analyses
and to better understand them. Thus, for example, the
assessment of the impact of policies could take place
more accurately (Tissot, 2014). At the same time, a
case study collection can serve as an overview so that
exchanges and collaborations among central banks
can be facilitated. In addition, Tissot (2014) mentions
another advantage: the added value compared to “tra-
ditional” statistics when central banks run respective
innovative projects – e.g., nowcasting and the use of
advanced analytics. In our research, beyond the liter-
ature positions, we conducted in-depth interviews that
elaborated potential benefits of a collection and we
identified relevant value dimensions. These will be
applied as objectives in a future evaluation.
Hence, we argue, that our research idea addresses
a relevant problem in the real-world environment.
From the description of the idea, we derive a high-
level research question that is at the core of the sys-
tematic review: Where in central banks (in which ap-
plication areas and departments) are advanced ana-
lytic methods (and which ones) applied to which spe-
cific issues?
The structuring and decomposition of the research
question is based on the literature, software libraries
(like Scikit-Learn and Tensor Flow), organizational
charts, library taxonomies, and interviews. Figure 4
shows an excerpt of the structuring of typical task ar-
eas of a central bank on the left. On the right, methods
and techniques of AI and ML are outlined (also in ex-
cerpts).
These frameworks allow a detailing of the re-
search question to work on it in a decentralized way
(in subprojects and by respective experts). Research
questions can now be derived from the two frame-
works by combining domain-specific areas and meth-
ods. For example, monetary policy in combination
with one or more regression methods. If necessary, it
may be appropriate to limit this further, for example,
to analyses in this area that refer to the real estate mar-
ket and examine related effects on price stability (if
studies are available at this level of detail). In either
case, the detailed research questions ultimately re-
main anchored in the high-level research question,
which allows the results to be combined at that level
(or an intermediate level).
ICSBT 2022 - 19th International Conference on Smart Business Technologies
140
Figure 4: Frameworks for decomposition of the research question.
Along the procedure shown in Figure 2, sub-re-
search questions have already been addressed in each
of several subprojects. The recommended databases
(e.g., Vom Brocke et al. 2009) were searched, alt-
hough manual searches were also carried out on spe-
cial databases and websites, for example those of the
Bank for International Settlements (BIS), due to the
special domain. After selecting and assessing the
quality of studies, a manageable number were se-
lected for deeper analysis.
Combining the results was done in “Summary of
Findings tables” (SoF). Other possibilities, e.g., sta-
tistical evaluation and combination in a meta-analy-
sis, were not considered, since mainly qualitative re-
sults are reported in the respective studies. It became
apparent that there is no “one fits all” format for the
SoF tables, i.e., that researchers have made modifica-
tions in each case, so that a consolidation of the vari-
ous tables in terms of the research question and the
research objective is a further task in the overall con-
text. The table in the appendix shows an excerpt of a
SoF table, here choosing from the framework “mone-
tary policy” on one side and “ML/AI” on the other.
The table also shows that a clear assignment to a task
area is not always possible. In various places, for ex-
ample, “monetary policy” is combined with other ar-
eas of responsibility (e.g., central bank communica-
tion).
Already with the results available, which in a way
could represent the final outcome of a Systematic Re-
view, it is now possible to switch again to the proce-
dure according to the DSRM. The SoF tables are pro-
totypes and thus, as artifacts, results of phase 3. These
can be used to carry out a demonstration and, against
the background of the objectives, an evaluation. For
the demonstration, this publication is a first step. It is
planned – in addition to an evaluation against the
background of the objectives – to involve possible ad-
dressees and stakeholders from central banks in order
to obtain their assessment of the usefulness, ease of
use and applicability. As is known from system de-
velopment, a prototype can be used as a stimulus to
make verification and validation as realistic as possi-
ble. In this respect, the reviews as partial results also
fulfil an essential purpose within the framework of
the design-oriented approach.
5 CONCLUSION AND FURTHER
RESEARCH
In this paper, a research project has been presented
that aims to demonstrate and investigate how and
whether systematic reviews can contribute to a better
sharing and exchange of knowledge. This is investi-
gated for the specific case of the central banking do-
main.
CentralBank
Tasks/Areas
Paymentsystems
Banking
supervision
Monetarypolicy
Financialstability
Cash
management
Centralbank
communication
ML/AI
Supervised
Learning
Regression
Linear
Polynomial
…
Classification
Decision Tree
SVM
…
Unsupvervised
learning
Clustering
…
Dimension
Reduction
…
Reinforcement
learning
…
Advanced Analytics in Central Banks: Basic Assumptions and Preliminary Results of a Research Project
141
The context is to identify and summarize
knowledge about the application of advanced analyt-
ics in central banks. A presentation of this knowledge
in the sense of a consolidation of the “body of
knowledge” can provide an overview of existing
work, stimulate own projects, and promote exchange
and cooperation among data scientists and developers
in central banks.
Our work also consists in a (methodological)
combination of DS and review research, as the result
should not only bring a progress of knowledge and
insight but also a very practical progress in the sense
of a case study database that should promote the ex-
change of interested organizations and participants.
In this position paper, only the basic idea has been
presented due to the fact that the methodological ap-
proach has not yet been fully defined and established,
and also in the sense that only preliminary results are
available so far. Reflections on the research process
have been presented above and it has already been
gone through several times. It is founded on estab-
lished work on review research and established ap-
proaches to design science. Against the background
of the special question pursued, a combination is
aimed at, which currently cannot yet be regarded as
well-founded in every respect. Further methodologi-
cal work is needed here. It is possible that the combi-
nation of design science and review research may
prove fruitful beyond the domain under considera-
tion.
Furthermore, beyond the still small-scale “proto-
types”, which each covers quite delimited thematic
areas, larger-scale reviews of studies and use cases
need to be carried out, so that not only the feasibility
is demonstrated, but also a concrete practical benefit.
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142
APPENDIX
Sub-results showing combination “monetary policy”
and “ML/AI” (all procedures)
#
Title Authors Year Central Bank Task Areas Methods and Techniques
of AI and ML
Research Question Outline / Results Research Setting
5
Between hawks and
doves: Measuring
central bank
communication
Tobback, Ellen;
Nardelli,
Stefano David
Martens
2017 ECB Monetary policy, Central
bank communication
Text Mining, Support-
Vector-Machines
(SVM), Latent Dirichlet
Allocation (LDA)
none explicitly
mentioned
Hawkish-Dovish (HD) indicator, which
measures the degree of "hawkishness" or
"dovishness" of the media's perception of
ECB's tone at each press conference.
Case Study
10
Central Bank
Communications and
the General Public
Haldane,
Andrew;
McMahon,
Michael
2018 Reserve Bank of
Australia
Monetary policy; Central
bank communication
Lineare Regression,
SVM & Random Forest
What does a high-
quality ZBK look like?
Perception of different
addressees.
There is no consensus on what high-
quality central bank communication should
look like. To shed light on this, 3 important
aspects are examined.
Case Study
21
Text mining for central
banks
Bholat, David;
Stephen,
Hansen;
Santos, Pedro;
Schonhardt-
Bailey, Cheryl
2015 Bank of England Monetary policy; Central
bank communication;
Financial stability
Text Mining, Supervised
Learning: Boolean
techniques, Dict.
techniques, weighting of
words, vector space
models, LDA
How can text mining
be useful for
addressing research
topics of interest to
central banks?
Discussion & step-by-step guide to text
mining, including an overview of
unsupervised and supervised techniques.
Report
22
The Evolving Scope
and Content of Central
Bank Speeches
Siklos, Pierre
L.; St. Amand,
Samantha;
Wajda, Joanna
2018 US Federal
Reserve and the
Bank of Canada
Monetary policy, Central
bank communication
NLP, LDA How have the use of
central bankers'
speeches and the
content of those
speeches changed
over time?
Assessment of Change.
Comparison between the use of speeches
by the Federal Reserve and the Bank of
Canada
Case Study
24
Topic classification of
Monetary Policy
Minutes from the
Swedish Central Bank
Cedervall,
Andreas;
Jansson,
Daniel
2018 Central Bank of
Sweden
Monetary policy, Central
bank communication
Neural Network, LDA none explicitly
mentioned
Analysis of Swedish Central Bank
protocols and collection of information
using latent Dirichlet allocation and a
simple neural network.
Case Study
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